Ha, Wooseok (하우석)

Ha, Wooseok (하우석) is an Assistant Professor in the Department of Mathematical Sciences at the Korea Advanced Institute of Science and Technology (KAIST). He previously served as a Machine Learning Scientist at AWS AI Labs and a Neyman Visiting Assistant Professor in the Department of Statistics at UC Berkeley. Ha earned his Ph.D. in Statistics from the University of Chicago, advised by Rina Foygel Barber. His research focuses on high-dimensional statistics and machine learning, including sparse and low-rank optimization, local graph clustering, and interpretable machine learning. He is also interested in applying statistical methods to fields such as medical imaging and population genetics. Ha is currently involved in the 'Glocal Lab' project at KAIST, focusing on AI-driven drug discovery and biohealth innovation.

Wooseok Ha is a researcher at KAIST with expertise in Statistics, Machine learning, and Optimization. His work focuses on developing advanced statistical methods and machine learning algorithms, particularly in areas such as optimization under constraints, statistical guarantees for clustering, and the application of large language models in code development. Ha's research also explores the theoretical foundations of deep learning, including the impact of stochastic gradient descent on autoencoder learning and the interpretation of neural networks. His publications highlight the use of techniques like wavelet distillation, variance-reduced methods, and Kullback–Leibler divergence constrained optimization to improve model performance and robustness.